Cost-effectiveness analyses of breast cancer screening have been used to evaluate clinical guidelines for conventional screening mammography. New imaging technologies, such as MRI and digital mammography, promise to detect breast cancer earlier than conventional mammography. Their high costs and the uncertainty about the magnitude of the benefits they can provide raise questions about their cost-effectiveness as an alternative to conventional screening mammography. We propose novel mathematical models that build on known physiological and epidemiological characteristics of breast cancer, along with preliminary information about test sensitivity and cost, to estimate the cost-effectiveness new imaging technologies. Our specific aims are to develop and validate novel computer models that simulate breast cancer screening for use in cost- effectiveness analysis, to propose a natural history model of ductal carcinoma in situ (DCIS) that can be incorporated into cost-effectiveness analysis, and to apply our models toward cost-effectiveness analyses of existing and emerging technologies for breast cancer screening.